⚡ Bolt: [성능 개선] computeTaskMetrics 루프 최적화 - #479
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O(N) 성능 개선을 위해 Map 캐싱과 함수형 배열 메서드(reduce/forEach)를 Float64Array 캐싱 및 전통적인 for 루프로 변경하여 가비지 컬렉션 및 해시 탐색 오버헤드를 제거했습니다.
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📝 WalkthroughWalkthrough
Changes작업 지표 반복 처리 최적화
Estimated code review effort: 2 (Simple) | ~10 minutes Possibly related PRs
🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing Touches 💡 1📝 Generate docstrings 💡
🧪 Generate unit tests (beta)
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🧹 Nitpick comments (1)
.jules/bolt.md (1)
7-9: 📐 Maintainability & Code Quality | 🔵 Trivial | ⚡ Quick win최적화 지침의 적용 범위를 숫자 캐시로 제한하세요.
현재 문구는 대규모 컬렉션 처리에서
Map보다Float64Array를 항상 우선하라고 지시합니다.Float64Array는 안정적인 배열 인덱스로 접근하는 연속 숫자 값에 적합합니다.byTask처럼task.id를 키로 조회하는 결과에는Map이 필요하며, 현재renderAll()과exportCsv()가 해당 조회를 사용합니다. 지침을 기간 캐시처럼 인덱스 기반 숫자 저장소에만 적용한다고 명시하세요.수정 예시
-**Learning:** For high-performance O(N) loops in JavaScript, replacing `Array.prototype.reduce`/`forEach` and `Map` caching with standard `for` loops and typed arrays (e.g., `Float64Array`) eliminates JS engine callback allocation, garbage collection, and hash-lookup overhead. -**Action:** Always prefer standard for-loops and typed arrays over functional array methods and Maps when processing large collections of objects in performance-critical paths. +**Learning:** For dense numeric caches indexed by stable task positions, standard `for` loops and typed arrays such as `Float64Array` can reduce callback and hash-lookup overhead. +**Action:** Use standard `for` loops and typed arrays for indexed numeric caches. Keep `Map` for task-ID lookups and other non-indexed keys.Based on learnings: 반복 ID 조회에는
Map을 먼저 구성해야 하므로 현재의 무조건적인Map회피 지침과 충돌하지 않게 범위를 명확히 해야 합니다.🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In @.jules/bolt.md around lines 7 - 9, Update the “Optimize O(N) tasks loops” guidance to limit typed-array preference to numeric caches indexed by stable array positions. Clarify that Map remains appropriate for key-based lookups such as byTask keyed by task.id, including the lookups used by renderAll() and exportCsv(), and should not be categorically avoided.Source: Learnings
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Nitpick comments:
In @.jules/bolt.md:
- Around line 7-9: Update the “Optimize O(N) tasks loops” guidance to limit
typed-array preference to numeric caches indexed by stable array positions.
Clarify that Map remains appropriate for key-based lookups such as byTask keyed
by task.id, including the lookups used by renderAll() and exportCsv(), and
should not be categorically avoided.
ℹ️ Review info
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Configuration used: Organization UI
Review profile: CHILL
Plan: Pro Plus
Run ID: 2473a054-6b41-4931-90f7-61fadb1882a9
📒 Files selected for processing (2)
.jules/bolt.mdapp.js
O(N) 성능 개선을 위해 Map 캐싱과 함수형 배열 메서드(reduce/forEach)를 Float64Array 캐싱 및 전통적인 for 루프로 변경하여 가비지 컬렉션 및 해시 탐색 오버헤드를 제거했습니다.
O(N) 성능 개선을 위해 Map 캐싱과 함수형 배열 메서드(reduce/forEach)를 Float64Array 캐싱 및 전통적인 for 루프로 변경하여 가비지 컬렉션 및 해시 탐색 오버헤드를 제거했습니다.
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Pull request overview
OpenCode cannot approve yet because required coverage evidence did not pass.
Review outcome
1. HIGH .github/workflows/opencode-review.yml:1 - Coverage evidence did not prove required test/docstring evidence
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Problem: The required coverage-evidence job result was
failure, so OpenCode cannot establish approval sufficiency for this head. -
Root cause: Automated approval is only valid when the same-head coverage-evidence job proves supported repository test suites passed and configured docstring gates passed or were advisory, or reports not applicable because no supported source files or package manifests exist. Missing, failed, skipped, unavailable, or unsupported-tooling test evidence is a blocker.
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Fix: Install or configure the repository test/docstring evidence tooling when source files or package manifests exist, rerun the current-head coverage-evidence job, and approve only after it reports
successwith required evidence or explicit no-source not-applicable evidence. -
Regression test: Keep the approval branch checking
needs.coverage-evidence.result == successbefore posting APPROVE, and publish REQUEST_CHANGES when coverage-evidence blocker states such as cancelled, skipped, failed, unsupported-tooling, or below-100 evidence are present. -
Result: REQUEST_CHANGES
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Reason: coverage-evidence result was
failure, so required test/docstring evidence was not proven for current head21756e00ba91fbe7ea2cae12817cee219d411174. -
Head SHA:
21756e00ba91fbe7ea2cae12817cee219d411174 -
Workflow run: 31701442952
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Workflow attempt: 1
Coverage evidence
Coverage Decision
- Result: FAIL
- Test evidence: not proven passing
- Docstring evidence: not proven passing when configured
- Failure count: 1
Changed-File Evidence Map
flowchart LR
PR["PR changed files"] --> Evidence["OpenCode bounded evidence"]
Evidence --> S1["Changed file (2 files)"]
S1 --> I1["repository behavior"]
I1 --> R1["Review risk: Changed file (2 files)"]
R1 --> V1["required checks"]
Evidence --> S2["Test: scopeweave.spec.js"]
S2 --> I2["regression suite"]
I2 --> R2["Review risk: Test: scopeweave.spec.js"]
R2 --> V2["targeted test run"]
OpenCode Review Overview
Pull request overviewOpenCode cannot approve yet because required coverage evidence did not pass. Review outcome1. HIGH .github/workflows/opencode-review.yml:1 - Coverage evidence did not prove required test/docstring evidence
Coverage evidenceCoverage Decision
Changed-File Evidence Mapflowchart LR
PR["PR changed files"] --> Evidence["OpenCode bounded evidence"]
Evidence --> S1["Changed file (2 files)"]
S1 --> I1["repository behavior"]
I1 --> R1["Review risk: Changed file (2 files)"]
R1 --> V1["required checks"]
Evidence --> S2["Test: scopeweave.spec.js"]
S2 --> I2["regression suite"]
I2 --> R2["Review risk: Test: scopeweave.spec.js"]
R2 --> V2["targeted test run"]
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Closing this duplicate implementation. Although #475 and #484 cover the same loop rewrite. No variant from this set will be merged until it demonstrates unchanged metric outputs and a material production-path improvement without weakening unrelated tests. |
Understood. Acknowledging that this work is now obsolete and stopping work on this task. |
1 participant
💡 무엇을
computeTaskMetrics함수 내Map기반 캐싱을Float64Array로 대체했습니다..reduce()와.forEach()배열 메서드를 전통적인for루프로 전환했습니다.🎯 왜
Map은 해시 탐색 오버헤드가 발생하며,reduce/forEach등의 함수형 배열 메서드는 매 반복마다 콜백을 할당해야 하므로, 데이터 양(O(N))이 많을 때 성능이 저하될 수 있습니다. 이를 더 빠른 타입 배열과 원시 루프로 변경하여 연산을 가속화하기 위함입니다.📊 영향
🔬 측정
test_metrics_perf.cjs벤치마크 테스트 스크립트를 사용하여 최적화 전/후 1000회 수행 기준 시간을 측정한 결과 성능 향상을 확인했습니다.PR created automatically by Jules for task 997725724518108268 started by @seonghobae
Summary by CodeRabbit
성능 개선
문서